Files
PINA/tutorials/tutorial22/tutorial.ipynb
Dario Coscia 9c60f616b7 tut22 (#637)
Co-authored-by: Federico Pichi <fpichi@sissa.it>
2025-09-15 19:31:38 +02:00

552 lines
204 KiB
Plaintext
Vendored
Raw Blame History

This file contains ambiguous Unicode characters
This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.
{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"id": "6f71ca5c",
"metadata": {},
"source": [
"# Tutorial: Reduced Order Model with Graph Neural Networks\n",
"\n",
"[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial22/tutorial.ipynb)\n",
"\n",
"\n",
"> ##### ⚠️ ***Before starting:***\n",
"> We assume you are already familiar with the concepts covered in the [Data Structure for SciML](https://mathlab.github.io/PINA/tutorial19/tutorial.html) tutorial. If not, we strongly recommend reviewing them before exploring this advanced topic.\n",
"\n",
"In this tutorial, we will demonstrate a typical use case of **PINA** for Reduced Order Modelling using Graph Convolutional Neural Network. The tutorial is largely inspired by the paper [A graph convolutional autoencoder approach to model order reduction for parametrized PDEs](https://www.sciencedirect.com/science/article/pii/S0021999124000111).\n",
"\n",
"Let's start by importing the useful modules:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0981f1e9",
"metadata": {},
"outputs": [],
"source": [
"## routine needed to run the notebook on Google Colab\n",
"try:\n",
" import google.colab\n",
"\n",
" IN_COLAB = True\n",
"except:\n",
" IN_COLAB = False\n",
"if IN_COLAB:\n",
" !pip install \"pina-mathlab[tutorial]\"\n",
" !wget \"https://github.com/mathLab/PINA/raw/refs/heads/master/tutorials/tutorial22/holed_poisson.pt\" -O \"holed_poisson.pt\"\n",
"\n",
"import torch\n",
"from torch import nn\n",
"from torch_geometric.nn import GMMConv\n",
"from torch_geometric.data import Data, Batch # alternatively, from pina.graph import Graph, LabelBatch\n",
"from torch_geometric.utils import to_dense_batch\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import warnings\n",
"\n",
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"from pina import Trainer\n",
"from pina.model import FeedForward\n",
"from pina.optim import TorchOptimizer\n",
"from pina.solver import ReducedOrderModelSolver\n",
"from pina.problem.zoo import SupervisedProblem"
]
},
{
"cell_type": "markdown",
"id": "c04276af",
"metadata": {},
"source": [
"## Data Generation\n",
"\n",
"In this tutorial, we will focus on solving the parametric **Poisson** equation, a linear PDE. The equation is given by:\n",
"\n",
"$$\n",
"\\begin{cases}\n",
"-\\frac{1}{10}\\Delta u = 1, &\\Omega(\\boldsymbol{\\mu}),\\\\\n",
"u = 0, &\\partial \\Omega(\\boldsymbol{\\mu}).\n",
"\\end{cases}\n",
"$$\n",
"\n",
"In this equation, $\\Omega(\\boldsymbol{\\mu}) = [0, 1]\\times[0,1] \\setminus [\\mu_1, \\mu_2]\\times[\\mu_1+0.3, \\mu_2+0.3]$ represents the spatial domain characterized by a parametrized hole defined via $\\boldsymbol{\\mu} = (\\mu_1, \\mu_2) \\in \\mathbb{P} = [0.1, 0.6]\\times[0.1, 0.6]$. Thus, the geometrical parameters define the left bottom corner of a square obstacle of dimension $0.3$. The problem is coupled with homogenous Dirichlet conditions on both internal and external boundaries. In this setting, $u(\\mathbf{x}, \\boldsymbol{\\mu})\\in \\mathbb{R}$ is the value of the function $u$ at each point in space for a specific parameter $\\boldsymbol{\\mu}$. \n",
"\n",
"We have already generated data for different parameters. The dataset is obtained via $\\mathbb{P}^1$ FE method, and an equispaced sampling with 11 points in each direction of the parametric space. \n",
"\n",
"The goal is to build a Reduced Order Model that given a new parameter $\\boldsymbol{\\mu}^*$, is able to get the solution $u$ *for any discretization* $\\mathbf{x}$. To this end, we will train a Graph Convolutional Autoencoder Reduced Order Model (GCA-ROM), as presented in [A graph convolutional autoencoder approach to model order reduction for parametrized PDEs](https://www.sciencedirect.com/science/article/pii/S0021999124000111). We will cover the architecture details later, but for now, lets start by importing the data.\n",
"\n",
"**Note:**\n",
"The numerical integration is obtained using a finite element method with the [RBniCS library](https://www.rbnicsproject.org/)."
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "9cbfd29d",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 400x400 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# === load the data ===\n",
"# x, y -> spatial discretization\n",
"# edge_index, triang -> connectivity matrix, triangulation\n",
"# u, params -> solution field, parameters\n",
"\n",
"data = torch.load(\"holed_poisson.pt\")\n",
"x = data['x']\n",
"y = data['y']\n",
"edge_index = data['edge_index']\n",
"u = data['u']\n",
"triang = data['triang']\n",
"params = data['mu']\n",
"\n",
"# simple plot\n",
"plt.figure(figsize=(4, 4))\n",
"plt.tricontourf(x[:, 10], y[:, 10], triang, u[:, 10], 100, cmap='jet')\n",
"plt.scatter(params[10, 0], params[10, 1], c='r', marker=\"x\", s=100)\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "f3619e4f",
"metadata": {},
"source": [
"## Graph-Based Reduced Order Modeling\n",
"\n",
"In this problem, the geometry of the spatial domain is **unstructured**, meaning that classical grid-based methods (e.g., CNNs) are not well suited. Instead, we represent the mesh as a **graph**, where nodes correspond to spatial degrees of freedom and edges represent connectivity. This makes **Graph Neural Networks (GNNs)**, and in particular **Graph Convolutional Networks (GCNs)**, a natural choice to process the data.\n",
"\n",
"<p align=\"center\">\n",
" <img src=\"http://raw.githubusercontent.com/mathLab/PINA/master/tutorials/static/gca_off_on_3_pina.png\" alt=\"GCA-ROM\" width=\"800\"/>\n",
"</p>\n",
"\n",
"To reduce computational complexity while preserving accuracy, we employ a **Reduced Order Modeling (ROM)** strategy (see picture above). The idea is to map high-dimensional simulation data $u(\\mathbf{x}, \\boldsymbol{\\mu})$ to a compact **latent space** using a **graph convolutional encoder**, and then reconstruct it back via a **decoder** (offline phase). The latent representation captures the essential features of the solution manifold. Moreover, we can learn a **parametric map** $\\mathcal{M}$ from the parameter space $\\boldsymbol{\\mu}$ directly into the latent space, enabling predictions for new unseen parameters.\n",
"\n",
"Formally, the autoencoder consists of an **encoder** $\\mathcal{E}$, a **decoder** $\\mathcal{D}$, and a **parametric mapping** $\\mathcal{M}$:\n",
"$$\n",
"z = \\mathcal{E}(u(\\mathbf{x}, \\boldsymbol{\\mu})), \n",
"\\quad\n",
"\\hat{u}(\\mathbf{x}, \\boldsymbol{\\mu}) = \\mathcal{D}(z),\n",
"\\quad\n",
"\\hat{z} = \\mathcal{M}(\\boldsymbol{\\mu}),\n",
"$$\n",
"where $z \\in \\mathbb{R}^r$ is the latent representation with $r \\ll N$ (the number of degrees of freedom) and the **hat notation** ($\\hat{u}, \\hat{z}$) indicates *learned or approximated quantities*.\n",
"\n",
"The training objective balances two terms:\n",
"1. **Reconstruction loss**: ensuring the autoencoder can faithfully reconstruct $u$ from $z$.\n",
"2. **Latent consistency loss**: enforcing that the parametric map $\\mathcal{M}(\\boldsymbol{\\mu})$ approximates the encoders latent space.\n",
"\n",
"The combined loss function is:\n",
"$$\n",
"\\mathcal{L}(\\theta) = \\frac{1}{N} \\sum_{i=1}^N \n",
"\\big\\| u(\\mathbf{x}, \\boldsymbol{\\mu}_i) - \n",
"\\mathcal{D}\\!\\big(\\mathcal{E}(u(\\mathbf{x}, \\boldsymbol{\\mu}_i))\\big) \n",
"\\big\\|_2^2\n",
"\\;+\\; \\frac{1}{N} \\sum_{i=1}^N\n",
"\\big\\| \\mathcal{E}(u(\\mathbf{x}, \\boldsymbol{\\mu}_i)) - \\mathcal{M}(\\boldsymbol{\\mu}_i) \\big\\|_2^2.\n",
"$$\n",
"This framework leverages the expressive power of GNNs for unstructured geometries and the efficiency of ROMs for handling parametric PDEs.\n",
"\n",
"We will now build the autoencoder network, which is a `nn.Module` with two methods: `encode` and `decode`.\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "3197831b",
"metadata": {},
"outputs": [],
"source": [
"class GraphConvolutionalAutoencoder(nn.Module):\n",
" def __init__(\n",
" self, hidden_channels, bottleneck, input_size, ffn, act=nn.ELU\n",
" ):\n",
" super().__init__()\n",
" self.hidden_channels, self.input_size = hidden_channels, input_size\n",
" self.act = act()\n",
" self.current_graph = None\n",
"\n",
" # Encoder GMM layers\n",
" self.fc_enc1 = nn.Linear(input_size * hidden_channels[-1], ffn)\n",
" self.fc_enc2 = nn.Linear(ffn, bottleneck)\n",
" self.encoder_convs = nn.ModuleList(\n",
" [\n",
" GMMConv(\n",
" hidden_channels[i],\n",
" hidden_channels[i + 1],\n",
" dim=1,\n",
" kernel_size=5,\n",
" )\n",
" for i in range(len(hidden_channels) - 1)\n",
" ]\n",
" )\n",
" # Decoder GMM layers\n",
" self.fc_dec1 = nn.Linear(bottleneck, ffn)\n",
" self.fc_dec2 = nn.Linear(ffn, input_size * hidden_channels[-1])\n",
" self.decoder_convs = nn.ModuleList(\n",
" [\n",
" GMMConv(\n",
" hidden_channels[-i - 1],\n",
" hidden_channels[-i - 2],\n",
" dim=1,\n",
" kernel_size=5,\n",
" )\n",
" for i in range(len(hidden_channels) - 1)\n",
" ]\n",
" )\n",
"\n",
" def encode(self, data):\n",
" self.current_graph = data\n",
" x = data.x\n",
" h = x\n",
" for conv in self.encoder_convs:\n",
" x = self.act(conv(x, data.edge_index, data.edge_weight) + h)\n",
" x = x.reshape(\n",
" data.num_graphs, self.input_size * self.hidden_channels[-1]\n",
" )\n",
" return self.fc_enc2(self.act(self.fc_enc1(x)))\n",
"\n",
" def decode(self, z, decoding_graph=None):\n",
" data = decoding_graph or self.current_graph\n",
" x = self.act(self.fc_dec2(self.act(self.fc_dec1(z)))).reshape(\n",
" data.num_graphs * self.input_size, self.hidden_channels[-1]\n",
" )\n",
" h = x\n",
" for i, conv in enumerate(self.decoder_convs):\n",
" x = conv(x, data.edge_index, data.edge_weight) + h\n",
" if i != len(self.decoder_convs) - 1:\n",
" x = self.act(x)\n",
" return x"
]
},
{
"cell_type": "markdown",
"id": "4d14d91d",
"metadata": {},
"source": [
"Great! We now need to build the graph structure (a PyTorch Geometric `Data` object) from the numerical solver outputs.\n",
"\n",
"The solver provides the solution values $u(\\mathbf{x}, \\boldsymbol{\\mu})$ for each parameter instance $\\boldsymbol{\\mu}$, along with the node coordinates $(x, y)$ of the unstructured mesh. Because the geometry is not defined on a regular grid, we naturally represent the mesh as a graph:\n",
"\n",
"- **Nodes** correspond to spatial points in the mesh. Each node stores the **solution value** $u$ at that point as a feature. \n",
"- **Edges** represent mesh connectivity. For each edge, we compute:\n",
" - **Edge attributes**: the relative displacement vector between the two nodes. \n",
" - **Edge weights**: the Euclidean distance between the connected nodes. \n",
"- **Positions** store the physical $(x, y)$ coordinates of the nodes.\n",
"\n",
"For each parameter realization $\\boldsymbol{\\mu}_i$, we therefore construct a PyTorch Geometric `Data` object:\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "8f098b6d",
"metadata": {},
"outputs": [],
"source": [
"# number of nodes and number of graphs (parameter realizations)\n",
"num_nodes, num_graphs = u.shape\n",
"\n",
"graphs = []\n",
"for g in range(num_graphs):\n",
" # node positions\n",
" pos = torch.stack([x[:, g], y[:, g]], dim=1) # shape [num_nodes, 2]\n",
" # edge attributes and weights\n",
" ei, ej = pos[edge_index[0]], pos[edge_index[1]] # [num_edges, 2]\n",
" edge_attr = torch.abs(ej - ei) # relative offsets\n",
" edge_weight = edge_attr.norm(p=2, dim=1, keepdim=True) # Euclidean distance\n",
" # node features (solution values)\n",
" node_features = u[:, g].unsqueeze(-1) # [num_nodes, 1]\n",
" # build PyG graph\n",
" graphs.append(\n",
" Data(\n",
" x=node_features,\n",
" edge_index=edge_index,\n",
" edge_weight=edge_weight,\n",
" edge_attr=edge_attr,\n",
" pos=pos,\n",
" )\n",
" )"
]
},
{
"cell_type": "markdown",
"id": "e38ad2d8",
"metadata": {},
"source": [
"## Training with PINA\n",
"\n",
"Everything is now ready! We can use **PINA** to train the model, following the workflow from previous tutorials. First, we need to define the problem. In this case, we will use the [`SupervisedProblem`](https://mathlab.github.io/PINA/_rst/problem/zoo/supervised_problem.html#module-pina.problem.zoo.supervised_problem), which expects: \n",
"\n",
"- **Input**: the parameter tensor $\\boldsymbol{\\mu}$ describing each scenario. \n",
"- **Output**: the corresponding graph structure (PyTorch Geometric `Data` object) that we aim to reconstruct. "
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "bbb3f90f",
"metadata": {},
"outputs": [],
"source": [
"problem = SupervisedProblem(params, graphs)"
]
},
{
"cell_type": "markdown",
"id": "79875c61",
"metadata": {},
"source": [
"Next, we build the **autoencoder network** and the **interpolation network**. \n",
"\n",
"- The **Graph Convolutional Autoencoder (GCA)** encodes the high-dimensional graph data into a compact latent space and reconstructs the graphs from this latent representation. \n",
"- The **interpolation network** (or parametric map) learns to map a new parameter $\\boldsymbol{\\mu}^*$ directly into the latent space, enabling the model to predict solutions for unseen parameter instances without running the full encoder."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "601b8b11",
"metadata": {},
"outputs": [],
"source": [
"reduction_network = GraphConvolutionalAutoencoder(\n",
" hidden_channels=[1, 1], bottleneck=8, input_size=1352, ffn=200, act=nn.ELU\n",
")\n",
"interpolation_network = FeedForward(\n",
" input_dimensions=2, output_dimensions=8, n_layers=2, inner_size=200, func=nn.Tanh\n",
")"
]
},
{
"cell_type": "markdown",
"id": "45f2d8b9",
"metadata": {},
"source": [
"Finally, we will use the [`ReducedOrderModelSolver`](https://mathlab.github.io/PINA/_rst/solver/supervised_solver/reduced_order_model.html#pina.solver.supervised_solver.reduced_order_model.ReducedOrderModelSolver) to perform the training, as discussed earlier. \n",
"\n",
"This solver requires two components: \n",
"- an **interpolation network**, which maps parameters $\\boldsymbol{\\mu}$ to the latent space, and \n",
"- a **reduction network**, which in our case is the **autoencoder** that compresses and reconstructs the graph data. "
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "47a02df1",
"metadata": {},
"outputs": [],
"source": [
"# This loss handles both Data and Torch.Tensors\n",
"class CustomMSELoss(nn.MSELoss):\n",
" def forward(self, output, target):\n",
" if isinstance(output, Data):\n",
" output = output.x\n",
" if isinstance(target, Data):\n",
" target = target.x\n",
" return torch.nn.functional.mse_loss(\n",
" output, target, reduction=self.reduction\n",
" )\n",
"\n",
"# Define the solver\n",
"solver = ReducedOrderModelSolver(\n",
" problem=problem,\n",
" reduction_network=reduction_network,\n",
" interpolation_network=interpolation_network,\n",
" use_lt=False,\n",
" loss=CustomMSELoss(),\n",
" optimizer=TorchOptimizer(torch.optim.Adam, lr=0.001, weight_decay=1e-05),\n",
")"
]
},
{
"cell_type": "markdown",
"id": "063b118a",
"metadata": {},
"source": [
"Training is performed as usual using the **`Trainer`** API. In this tutorial, we will use only **30% of the data** for training, and only $300$ epochs of training to illustrate the workflow."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7081ca73",
"metadata": {},
"outputs": [],
"source": [
"trainer = Trainer(\n",
" solver=solver,\n",
" accelerator=\"cpu\",\n",
" max_epochs=300,\n",
" train_size=0.3,\n",
" val_size=0.7,\n",
" test_size=0.,\n",
" shuffle=True,\n",
")\n",
"trainer.train()"
]
},
{
"cell_type": "markdown",
"id": "b1d11289",
"metadata": {},
"source": [
"Once the model is trained, we can test the reconstruction by following two steps:\n",
"\n",
"1. **Interpolate**: Use the `interpolation_network` to map a new parameter $\\boldsymbol{\\mu}^*$ to the latent space. \n",
"2. **Decode**: Pass the interpolated latent vector through the autoencoder (`reduction_network`) to reconstruct the corresponding graph data."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "8dd5c0d4",
"metadata": {},
"outputs": [],
"source": [
"# interpolate\n",
"z = interpolation_network(params)\n",
"\n",
"# decode\n",
"batch = Batch.from_data_list(graphs)\n",
"out = reduction_network.decode(z, decoding_graph=batch)\n",
"out, _ = to_dense_batch(out, batch.batch)\n",
"out = out.squeeze(-1).T.detach()"
]
},
{
"cell_type": "markdown",
"id": "91685b70",
"metadata": {},
"source": [
"Let's compute the total error, and plot a sample solution:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "29d3dbac",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"L2 relative error 6.90%\n"
]
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1600x400 with 6 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# compute error\n",
"l2_error = (torch.norm(out - u, dim=0) / torch.norm(u, dim=0)).mean()\n",
"print(f\"L2 relative error {l2_error:.2%}\")\n",
"\n",
"# plot solution\n",
"idx_to_plot = 42\n",
"# Determine min and max values for color scaling\n",
"vmin = min(out[:, idx_to_plot].min(), u[:, idx_to_plot].min())\n",
"vmax = max(out[:, idx_to_plot].max(), u[:, idx_to_plot].max())\n",
"plt.figure(figsize=(16, 4))\n",
"plt.subplot(1, 3, 1)\n",
"plt.tricontourf(\n",
" x[:, idx_to_plot],\n",
" y[:, idx_to_plot],\n",
" triang,\n",
" out[:, idx_to_plot],\n",
" 100,\n",
" cmap=\"jet\",\n",
" vmin=vmin,\n",
" vmax=vmax,\n",
")\n",
"plt.title('GCA-ROM')\n",
"plt.colorbar()\n",
"plt.subplot(1, 3, 2)\n",
"plt.title('True')\n",
"plt.tricontourf(\n",
" x[:, idx_to_plot],\n",
" y[:, idx_to_plot],\n",
" triang,\n",
" u[:, idx_to_plot],\n",
" 100,\n",
" cmap=\"jet\",\n",
" vmin=vmin,\n",
" vmax=vmax,\n",
")\n",
"plt.colorbar()\n",
"plt.subplot(1, 3, 3)\n",
"plt.title('Square Error')\n",
"plt.tricontourf(x[:, idx_to_plot], y[:, idx_to_plot], triang, (u-out).pow(2)[:, idx_to_plot], 100, cmap='jet')\n",
"plt.colorbar()\n",
"plt.ticklabel_format()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "c152bfd1",
"metadata": {},
"source": [
"Nice! We can see that the network is correctly learning the solution operator, and the workflow was very straightforward. \n",
"\n",
"You may notice that the network outputs are not as smooth as the actual solution. Dont worry — training for longer (e.g., ~5000 epochs) will produce a smoother, more accurate reconstruction.\n",
"\n",
"## What's Next?\n",
"\n",
"Congratulations on completing the introductory tutorial on **Graph Convolutional Reduced Order Modeling**! Now that you have a solid foundation, here are a few directions to explore:\n",
"\n",
"1. **Experiment with Training Duration** — Try different training durations and adjust the network architecture to optimize performance. Explore different integral kernels and observe how the results vary.\n",
"\n",
"2. **Explore Physical Constraints** — Incorporate physics-informed terms or constraints during training to improve model generalization and ensure physically consistent predictions.\n",
"\n",
"3. **...and many more!** — The possibilities are vast! Continue experimenting with advanced configurations, solvers, and features in PINA.\n",
"\n",
"For more resources and tutorials, check out the [PINA Documentation](https://mathlab.github.io/PINA/)."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "pina",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.18"
}
},
"nbformat": 4,
"nbformat_minor": 5
}